As computing power becomes increasingly decentralized in the name of efficiency and scalability, data storage capabilities are also moving to a distributed, decentralized model in which storage servers or other devices are allocated in such a way as to replicate a given chunk of data across multiple such servers or devices so as to increase durability, scalability, performance, and the like. Due to the increasingly massive nature of such distributed data storage systems, tracking consumption of services provided therefrom becomes increasingly complex. For example, in distributed storage systems, as a result of time lag when distributing or replicating data across multiple storage devices, the storage devices may store a different quantity, content, or version of the data at a given point in time, and accurately obtaining various types of information related to the data (such as the quantity thereof) may be difficult. As greater numbers of customers utilize distributed data storage systems in an ever-increasing slate of applications, efficient methods for metering data stored thereon to, for example, correctly charge the appropriate customer for usage thereof, become important.